1. Field of the Invention
The present invention relates to information processing techniques, and in particular to an object tracker for tracking an object in an input image and an object tracking method performed therein.
2. Description of the Related Art
Visual tracking is essential for many applications such as computer vision, human-machine interfacing, and human-human interfacing. Computer vision is especially focused in security technologies such as visual surveillance, and audio and visual technologies such as analysis, classification, and editing of recorded images. Human-human interfacing includes teleconferencing and videotelephony. Accordingly, there have been many studies undertaken on visual tracking, with a number of those specifically addressing tracking accuracy and processing efficiency. A major approach to visual tracking is now based on a particle filter. The particle filter attracts attention as a time series analysis tool for systems with non-Gaussian noise, which the well known Kalman filter cannot deal with. The CONDENSATION algorithm (Conditional Density Propagation) is well known as a technique based on a particle filter (see Michael Isard and Andrew Blake: Contour tracking by stochastic propagation of conditional density, Proc. European Conf. Computer Vision, vol. 1, 1996, pp. 343-356, Cambridge, UK, and Michael Isard and Andrew Blake: CONDENSATION—conditional density propagation for visual tracking, Int. Conf. J. Computer Vision, 29, 1, 5-28 (1998), as examples).
The particle filter is a computation method for the approximation of the Bayesian filter, and represents the probability distribution of a target object by introducing a finite number of particles as target candidates. The probability distribution of the target object is used for time series estimations and predictions. When the particle filter is used for visual tracking, the motion of a parameterized object is described as one particle, and the particle filter sequentially estimates the probability distributions of the object in the parameter space by parameter transition based on motion models and observation for calculating the likelihood of the transition results. However, the particle filter is relatively less effective for the motion of an object that cannot be fully represented by a preconfigured motion model. Therefore, the success of the particle filter largely depends on how a motion model is constructed.
When using conventional standard particle filters for visual tracking, a motion models is selected from various motion models depending on the type of image being observed. However, tracking using a single motion model requires fixed characteristics of object motion in the parameter space and thus is applicable only in limited circumstances. This is because the tracking performance drops significantly when the single motion model cannot properly describe the object motion within the parameter space. In contrast, a tracking technique that switches between multiple motion models is proposed (for example, see Michael Isard and Andrew Blake. A mixed-state CONDENSATION tracker with automatic mode-switching, Proc. 6th Int. Conf. Computer Vision, 1988). This tracking technique, however, is impractical as it requires learning of the switching timing and it is effective only when the learning result converges.